At present, the multispectral remote sensing data obtained by UAV (Unmanned Aerial Vehicle), have been widely used in the quantitative monitoring of agriculture, forest, environment and other fields. However, the existing methods of converting the multispectral remote sensing data into land surface reflectance still have some defects, e.g. relying on reference boards, unable to adapt to varying solar illumination conditions, or the obtained results are inaccurate, etc., so they may affect the quantitative application effect of the remote sensing data. In order to solve this problem, a novel methodology for direct measurement of land surface reflectance with the multispectral camera is proposed in this study. The method has strong adaptability, and it can get accurate land surface reflectance even under the condition of variable illumination. The key problem that needs to be solved is how to use irradiance sensors to obtain accurate solar irradiance in a tilted state. A new method was proposed to separate the direct and scattering irradiance with two or more irradiance sensors oriented towards different directions to achieve this objective. Therefore, the digital numbers (DNs) recorded by the multispectral bands can be converted into accurate reflectance with the measured irradiance results. UAV-based remote sensing images obtained on the different dates under different illumination conditions were used to validate the actual effect through the validation scheme designed in this study. The experimental results showed that the maximum MAE (mean absolute error) and standard deviation of five multispectral bands (i.e. blue, green, red, red edge and near infrared) for all reference panels is 3.34% and 2.11%, respectively; and the maximum MAE and standard deviation of the three reference panels (i.e. black, gray and white) is 2.94% and 1.84%, respectively. Therefore, the proposed method can obtain accurate reflectance results, even under varying solar illumination conditions. It greatly simplifies the reflectance conversion process of UAV-based remote sensing images. The study results are significant to the UAV-based remote sensing system's design and the quantitative application of multispectral remote sensing data.